A new version of the collaborative filtering talk: - Presenting the Netflix Prize story - Discussing User-based and Item-based collaborative filtering, and various similarity metrics - Discussing how to Map-Reduce the calculation

1. WE KNOW YOU WILL LIKE THIS
Introduction to Recommendation Engines
Monday, January 14, 13

2. ML
X X +Y
Supervised Unsupervised
Clustering
T + YT
X X +Y
Hierarchical Clustering
Regression Classification
Turnout Class
30 Spam
Y= (numeric) Y = Not Spam (Categorical)
12
25 Spam
Monday, January 14, 13

3. MarabooKarnaf Ima Adama
Liv
Idan 5 ? 3 ?
Shahar 4 3 ? 2
Gadi ? 1 ? 5
Content/Model-Based
(Agnostic, Behavioural)
(predicting the rating)
Recommendation
Monday, January 14, 13

4. Monday, January 14, 13

5. Monday, January 14, 13

6. Monday, January 14, 13

7. Monday, January 14, 13

8. Monday, January 14, 13

9. Monday, January 14, 13

10. Monday, January 14, 13

11. Monday, January 14, 13

12. Preference Problem (Ads)
Rating Problem (Movies)
Monday, January 14, 13

13. Monday, January 14, 13

14. Related problem: Ranking
Monday, January 14, 13

15. Maraboo Karnaf Ima Adama Liv
Idan 1 ? 1 ?
Shahar 1 1 ? 1
Gadi ? 1 ? 1
Maraboo Karnaf Ima Adama Liv
Idan 5 ? 3 ?
Shahar 4 3 ? 2
Gadi ? 1 ? 5
Monday, January 14, 13

16. Maraboo Karnaf Ima Adama Liv
Idan 1 ? 1 ?
Shahar 1 1 ? 1
Gadi ? 1 ? 1
Monday, January 14, 13

17. Maraboo Karnaf Ima Adama Liv
Idan 5 ? 3 ?
Shahar 4 3 ? 2
Gadi ? 1 ? 5
Monday, January 14, 13

18. User-based Collaborative Filtering
Monday, January 14, 13

19. Monday, January 14, 13

20. Jaccard Distance “We share 5 preferences out of 7!”
Euclidean Distance
Cosine Similiarity
Pearson’s
Correlation 1- “Our preferences go
Distance in the same direction!”
(but only 2 such preferences do...)
Log-Likelihood
Ratio
Measure of “Surprise” at correlation
Monday, January 14, 13

21. Item-Based Collaborative Filtering
Usually bounded
Monday, January 14, 13

22. Case study: Amazon
100,000,000 users
2,000,000 items
Each user expresses preference for 10 items
Each item has 500 reviews
User-Based CF: Item-Based CF:
100,000,000 x 100,000,000 2,000,000 x 2,000,000 similarity
similarity matrix matrix
2,000,000 x 500 sum terms 2,000,000 x 10 sum terms
Monday, January 14, 13

23. Interpretability
“People who go to
La Colombe “Coffee Shop
Torrefaction & connoisseurs tend
FourSquare HQ tend to come here”
to go here”
Monday, January 14, 13

24. Evaluation
Rating Problem: Predictive accuracy (regression) metrics
RMSE, MAE, etc.
Preference (Binary) Problem: Classification accuracy (IR) metrics
Accuracy, Precision, Recall, F-1, ROC, etc.
Benchmark vs. ‘random’ and ‘popular’
Ranking accuracy metrics: Similarity of permutations
Pearson’s correlation, Spearman’s rho, Kendall’s tau
Monday, January 14, 13

25. Monday, January 14, 13

26. Challenges
Cold-start problems (new item, new user)
“Black” and “Grey” sheep
Exploration-exploitation and reinforcement learning
Scale
Monday, January 14, 13

27. Advanced Topics
Dimensionality Reduction
Map-Reducible calculations
Content-based (feature-based)
Multiple models
Monday, January 14, 13

28. MapReduce Similarity Calculation
“User-based”
A ui
Maraboo Karnaf Ima Adama Liv Gadi Gadi
Idan
Shahar
1
1
?
1
1
?
?
1 * Maraboo
Karnaf
?
1
= Idan
Shahar
0
2
Gadi ? 1 ? 1 Ima Adama ? Gadi 2
Liv 1
User similarity vector
AT Aui T(Au )
Maraboo
Idan
1
Shahar Gadi
1 ?
* Idan
Gadi
0
= Maraboo
Gadi
2
A i
Karnaf ? 1 1 Shahar 2 Karnaf 4
Ima Adama 1 ? ? Gadi 2 Ima Adama 0
Liv ? 1 1 Liv 4
Monday, January 14, 13

29. MapReduce Similarity Calculation
“Item-Based”
A T A
Idan Shahar Gadi Maraboo Karnaf Ima Adama Liv Maraboo Karnaf Ima Adama Liv
Maraboo 1 1 ? Idan 1 ? 1 ?
Karnaf ? 1 1 * Shahar 1 1 ? 1 = Maraboo
Karnaf
2
1
1
2
1
0
1
2
Ima Adama 1 ? ? Gadi ? 1 ? 1 Ima Adama 1 0 1 0
Liv ? 1 1 Liv 1 2 0 2
Item similarity matrix
ATA ui
Maraboo Karnaf Ima Adama Liv Gadi Gadi
Maraboo 2 1 1 1 Maraboo ? Maraboo 2
=
* T
(A A)ui
Karnaf 1 2 0 2 Karnaf 1 Karnaf 4
Ima Adama 1 0 1 0 Ima Adama ? Ima Adama 0
Liv 1 2 0 2 Liv 1 Liv 4
Similarity of item x to item y is <ix,iy>
Monday, January 14, 13

30. MapReduce Similarity Calculation
Recall row outer-product matrix multiplication:
Maraboo Karnaf Ima Adama Liv
Maraboo 2 1 1 1
Karnaf 1 2 0 2
Ima Adama 1 0 1 0
Liv 1 2 0 2
=
Maraboo Karnaf Ima Adama Liv Maraboo Karnaf Ima Adama Liv Maraboo Karnaf Ima Adama Liv
Maraboo 1 0 1 0 Maraboo 1 1 0 1 Maraboo 0 0 0 0
Karnaf
Ima Adama
0
1
0
0
0
1
0
0 + Karnaf
Ima Adama
1
0
1
0
0
0
1
0
+ Karnaf
Ima Adama
0
0
1
0
0
0
1
0
Liv 0 0 0 0 Liv 1 1 0 1 Liv 0 1 0 1
uIdanuIdan T uShaharuShahar
T uGadiuGadi T
Only one user’s list of items is used every time!
Monday, January 14, 13

31. MapReduce Similarity Calculation
All of the classic similarity functions are
made up of 3 stages:
Preprocess (uses only one ELEMENT)
Norm (Can be done in reduce on one
VECTOR)
T
Similarity utilizes the A A matrix joined
with norm entries
Monday, January 14, 13

32. Bibliography
Google News Personalization: Scalable Online Collaborative Filtering - Das, Datar, Garg, Rajaram, WWW2007
Logistic Regression and Collaborative Filtering for Sponsored Search Term Recommendation - Bartz, Murthi, Sebastian, EC2006
Evaluating Collaborative Filtering Recommender Systems - Herlocker, Konstan, Tenveen, Riedl, ACM TIS2004
A Survey of Collaborative Filtering Techniques - Su, Khoshgoftaar, AAI2009
An Introduction to Information Retrieval - Manning, Raghavan, Schutze, Cambridge Press
Mahout in Action - Friedman, Dunning, Anil, Owen, Manning Publications
Lessons from the Netflix Prize Challenge - Bell, Koren, KDD2009
Factorization meets the Neighbourhood: a Multifaceted Collaborative Filtering Model - Koren, KDD2008
Accurate Methods for the Statistics of Surprise and Coincidence - Dunning, ACL1993
Item-Based Collaborative Filtering Recommendation Algorithms - Sarwar, Konstan, Karypis, Riedl, WWW2001
Matrix Factorization Techniques for Recommender Systems - Koren, Bell, Volinsky, IEEE2009
recommenderlab: A Framework for Developing and Testing Recommendation Algorithms - Hahsler, 2001
Scalable Similarity-Based Neighbourhood Methods with MapReduce - Schelter, Boden, Markl, RecSys2012
Monday, January 14, 13

33. Thanks!
Nimrod Priell
nimrod.priell@gmail.com
@nimrodpriell
http://www.educated-guess.com
Monday, January 14, 13